SPC floor noise-proof structural acoustic performance evaluation method and system

Abstract

The present invention relates to the technical field of building acoustic performance evaluation, and in particular to a method and system for evaluating the noise-proof structural acoustic performance of an SPC floor. The method comprises: collecting the acoustic structural properties of the SPC floor, and establishing a plurality of structural acoustic evaluation factors according to the acoustic performance evaluation requirements of the SPC floor, respectively allocating acoustic response weights to the plurality of structural acoustic evaluation factors, and respectively allocating combined secondary weights to the plurality of structural acoustic evaluation factors according to the acoustic structural properties, and finally constructing an acoustic evaluation model. An evaluation result is output by combining the acoustic response weights with the combined secondary weights. The present invention effectively solves the problem of lacking a dynamic adjustment mechanism for evaluation weights in traditional acoustic evaluations, strengthens the continuity of responses to material and structural changes, reduces costs, and accurately predicts acoustic effects.

Description

Technical Field

[0001] The present invention relates to the technical field of building acoustic performance evaluation, and in particular to a method and system for evaluating the acoustic performance of an SPC floor noise-proof structure. Background Art

[0002] In the building materials and interior design industries, SPC (Stone Plastic Composite) flooring is widely popular for its excellent noise reduction performance and durability. With the improvement of environmental awareness and the increasing demand of consumers for comfortable living environments, efficient acoustic performance evaluation methods have become key to the development of the industry. SPC flooring manufacturers and designers are constantly seeking more advanced evaluation tools and methods to ensure that new products can meet strict consumer standards in a highly competitive market.

[0003] However, although existing technologies can provide basic acoustic performance analysis, there are still deficiencies in how to systematically integrate historical data, evaluate weights, and implement continuous optimization. Traditional methods often rely on data collection at a single point in time, ignoring the value of historical performance data in continuous improvement. In addition, the lack of a mechanism for dynamically adjusting evaluation weights means that acoustic performance evaluation often cannot reflect the actual changes in material and structural properties over time, thereby limiting the accuracy and practicality of the evaluation results.

[0004] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present disclosure and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art known to those skilled in the art. Summary of the Invention

[0005] The present invention provides a method and system for evaluating the acoustic performance of an SPC floor noise-proof structure, which can effectively solve the problems in the background technology.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] A method for evaluating the acoustic performance of SPC floor noise-proof structures, the method comprising:

[0008] Acoustic structural properties of the SPC floor are collected, and several structural acoustic evaluation factors are established according to the acoustic performance evaluation requirements of the SPC floor, wherein the acoustic structural properties include noise-proof material properties and noise-proof structural properties;

[0009] assigning acoustic response weights to the plurality of structural acoustic assessment factors respectively, and assigning combined secondary weights to the plurality of structural acoustic assessment factors respectively according to the acoustic construction properties;

[0010] An acoustic evaluation model is constructed, and a structural acoustic performance evaluation result is output according to the acoustic evaluation model in combination with the acoustic response weight and the combined secondary weight.

[0011] Furthermore, acoustic response weights are assigned to the structural acoustic assessment factors, including:

[0012] Collecting historical attribute parameters of the SPC floor, assigning initial evaluation weights according to the historical attribute parameters of the SPC floor and a number of the structural acoustic evaluation factors, and calculating the initial total score according to the initial evaluation weights;

[0013] Based on the initial evaluation weight, mark any one of the structural acoustics evaluation factors as a variable factor and the remaining structural acoustics evaluation factors as constant factors, increase or decrease the initial evaluation weight of the variable factor, calculate a total score, and compare the total score with the initial total score to obtain a change in the total score;

[0014] Cancel the mark of the variable factor, select any of the constant factors as the variable factor, set the canceled variable factor as the constant factor again, increase or decrease the initial evaluation weight of the variable factor respectively, calculate the total score, and compare it with the initial total score to obtain the change in the total score;

[0015] Repeating the step of marking the variable factors until all the structural acoustics assessment factors serve as the variable factors, and obtaining the total score changes corresponding to the variable factors;

[0016] According to the total score change, as the influence of the plurality of structural acoustics assessment factors on the structural acoustics performance assessment, acoustic response weights are allocated according to the influence.

[0017] Furthermore, calculating the initial total score according to the initial evaluation weights includes:

[0018] S1: Collect historical acoustic test parameters, perform performance analysis on the historical acoustic test parameters according to a control variable method, obtain performance analysis results, assign scores to several structural acoustic evaluation factors based on the performance analysis results, and obtain individual scores corresponding to the several structural acoustic evaluation factors;

[0019] S2: integrating the individual scores corresponding to the plurality of structural acoustics assessment factors with the initial assessment weights to obtain the individual contributions corresponding to the structural acoustics assessment factors, and combining the individual contributions corresponding to the plurality of structural acoustics assessment factors to obtain an initial total score;

[0020] The total score change is obtained by calculating the total score according to steps S1 and S2, and calculating the difference between the total score and the initial total score to obtain the total score change.

[0021] Furthermore, according to the acoustic construction properties, a combination of secondary weights is assigned to the plurality of structural acoustic assessment factors, including:

[0022] Establishing several secondary acoustic evaluation factors according to the properties of the noise-proof material and the noise-proof structure, wherein the secondary acoustic evaluation factors include the ratio of natural stone powder to polyvinyl chloride, and the proportions of the core layer, the wear-resistant layer, and the bottom layer;

[0023] An acoustic influence standard is set using a scaling method, and the ratio of the natural stone powder to the polyvinyl chloride and the proportion of the core layer, the wear-resistant layer, and the bottom layer are evaluated according to the acoustic influence standard to obtain an evaluation value, wherein the evaluation is to separately evaluate the material properties of the natural stone powder and the polyvinyl chloride and the structural properties of the proportion of the core layer, the wear-resistant layer, and the bottom layer;

[0024] constructing secondary factor evaluation matrices according to the evaluation values, wherein the rows and columns of the secondary factor evaluation matrix are the secondary acoustic evaluation factors, and the element values in the matrix are the evaluation values;

[0025] Calculating the maximum eigenvalue of the secondary factor evaluation matrix using a mathematical method, obtaining a corresponding eigenvector according to the maximum eigenvalue, normalizing the eigenvector to obtain a normalized eigenvector, wherein several element values in the normalized eigenvector represent secondary evaluation weights of the secondary acoustic evaluation factors;

[0026] combining the secondary evaluation weight of the noise-proofing material property with the secondary evaluation weight of the noise-proofing structure property to obtain a combined secondary weight;

[0027] Furthermore, an acoustic evaluation model is constructed, including:

[0028] Constructing a weight verification database, wherein the weight verification database includes the historical attribute parameters of the SPC floor and the historical acoustic test parameters;

[0029] Extracting a mapping relationship between the historical attribute parameters of the SPC floor and the historical acoustic test parameters according to the weight verification database, and obtaining a plurality of historical acoustic response weights and historical combined secondary weights according to the mapping relationship;

[0030] Performing a deviation analysis on the acoustic response weight and the combined secondary weight according to the historical acoustic response weight and the historical combined secondary weight, optimizing the acoustic response weight and the combined secondary weight according to the deviation analysis result, obtaining an optimization result, and constructing an acoustic evaluation model according to the optimization result.

[0031] Furthermore, a weight verification database is constructed, including:

[0032] Extracting historical material attribute information and historical structural attribute information based on the historical attribute parameters of the SPC floor to obtain an acoustic evaluation feature set;

[0033] Performing deep learning on the acoustic evaluation feature set using a machine learning algorithm to obtain a deep learning result;

[0034] Determine a standard material ratio and a standard structure proportion based on the deep learning result, and assign a secondary weight of the historical combination based on the standard material ratio and the standard structure proportion;

[0035] Performing secondary learning on the historical combined secondary weights and the historical acoustic test parameters to obtain historical acoustic response weights, and establishing a mapping relationship between the historical combined secondary weights and the historical acoustic response weights;

[0036] The mapping relationship and historical acoustic test parameters are combined and indexed to construct a weight verification database.

[0037] Furthermore, performing a deviation analysis on the acoustic response weight and the combined secondary weight according to the historical acoustic response weight and the historical combined secondary weight, and optimizing the acoustic response weight and the combined secondary weight according to the deviation analysis result, includes:

[0038] The historical acoustic response weight and the historical combined secondary weight are respectively used as standard values, and the acoustic response weight and the combined secondary weight are respectively used as actual values, and deviation analysis is performed respectively using a loss function to obtain deviation analysis results;

[0039] Identifying main deviation factors according to the deviation results, re-evaluating weights of the main deviation factors, and performing deviation analysis again using the loss function according to the re-evaluation results to obtain secondary deviation analysis results;

[0040] Iterative optimization is performed based on the secondary deviation analysis result to obtain an optimization result.

[0041] Furthermore, the deviation analysis is performed using loss functions, including:

[0042] ;

[0043] in, represents the actual value, Indicates the standard value, is the number of acoustic response weights or combined sub-weights, Indicates the deviation analysis results.

[0044] SPC floor noise-proof structural acoustic performance evaluation system, the system includes:

[0045] An evaluation factor setting module collects the acoustic structural properties of the SPC floor and establishes several structural acoustic evaluation factors based on the acoustic performance evaluation requirements of the SPC floor. The acoustic structural properties include noise-proof material properties and noise-proof structural properties.

[0046] a multi-weight assignment module for assigning acoustic response weights to each of the plurality of structural acoustic assessment factors, and assigning combined secondary weights to each of the plurality of structural acoustic assessment factors according to the acoustic construction properties;

[0047] The model construction and output module constructs an acoustic evaluation model and outputs a structural acoustic performance evaluation result according to the acoustic evaluation model in combination with the acoustic response weight and the combined secondary weight.

[0048] Furthermore, the model building and output module includes:

[0049] A weight verification database construction unit is configured to construct a weight verification database, wherein the weight verification database includes the historical attribute parameters of the SPC floor and the historical acoustic test parameters;

[0050] a historical weight data extraction unit, which extracts a mapping relationship between the historical attribute parameters of the SPC floor and the historical acoustic test parameters according to the weight verification database, and obtains a plurality of historical acoustic response weights and historical combined secondary weights according to the mapping relationship;

[0051] The weight deviation analysis and optimization unit performs deviation analysis on the acoustic response weight and the combined secondary weight according to the historical acoustic response weight and the historical combined secondary weight, optimizes the acoustic response weight and the combined secondary weight according to the deviation analysis result, obtains the optimization result, and constructs the acoustic evaluation model according to the optimization result.

[0052] The technical solution of the present invention can achieve the following technical effects:

[0053] This method effectively solves the problem of the lack of a dynamic adjustment mechanism for evaluation weights in traditional acoustic evaluations, thereby significantly improving the accuracy and practicality of acoustic performance evaluations. By utilizing historical performance data, this method not only strengthens the continuity of responses to material and structural changes, but also can adjust the evaluation model in real time to ensure that the evaluation results can reflect the latest situation in actual applications. This efficient evaluation process not only shortens the product development cycle and reduces costs, but also accurately predicts acoustic effects.

[0054] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0056] Figure 1 This is a flow chart of the method for evaluating the noise-proof structural acoustic performance of SPC flooring;

[0057] Figure 2 Schematic diagram of the process for assigning acoustic response weights;

[0058] Figure 3 Schematic diagram of the process for calculating the total score;

[0059] Figure 4 Model structure diagram for acoustic evaluation;

[0060] Figure 5 Assigning acoustic response weights to the structure diagram. DETAILED DESCRIPTION

[0061] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0062] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0063] Embodiment 1;

[0064] like Figure 1 As shown, the present application provides a method for evaluating the acoustic performance of the noise-proof structure of an SPC floor, the method comprising:

[0065] S100: Collect the acoustic structural properties of the SPC floor and establish several structural acoustic evaluation factors based on the acoustic performance evaluation requirements of the SPC floor. The acoustic structural properties include the properties of the noise-proof material and the noise-proof structure.

[0066] S200: assigning acoustic response weights to a plurality of structural acoustic assessment factors, and assigning combined secondary weights to the plurality of structural acoustic assessment factors according to acoustic structural properties;

[0067] S300: Construct an acoustic evaluation model, and output a structural acoustic performance evaluation result based on the acoustic evaluation model in combination with the acoustic response weight and the combined secondary weight.

[0068] Specifically, the acoustic structural properties of the SPC floor are first comprehensively collected, including the properties of the noise-proof materials, such as the density, thickness and type of the materials, PVC, fillers, etc., and the noise-proof structural properties, such as the hierarchical structure, the combination of each layer of materials and its acoustic isolation characteristics. According to these performance parameters and the acoustic performance evaluation requirements of the SPC floor, such as sound absorption coefficient, warm-up performance, etc., a number of structural acoustic evaluation factors are established, and each structural acoustic evaluation factor is assigned an acoustic response weight. These weights can be based on empirical data obtained from similar applications and test results and the advice of acoustic experts. In addition, the combined secondary weight is assigned according to the interaction between the noise-proof material and the structural properties to take into account the mutual influence between the material and structural characteristics, so as to achieve the effect of sound wave transmission. Taking the transmission loss as an example, considering its importance in residential floors, the sound wave transmission loss is given a higher acoustic response weight. Taking into account the noise-proof material properties of the high-density polyvinyl chloride (PVC) and stone powder mixture used, an additional secondary weight is added to the evaluation factor directly related to the material density, and the weight distribution is further refined. Then, a comprehensive acoustic evaluation model is constructed using the assigned acoustic response weights and combined secondary weights. The model adopts modern acoustic theories and calculation methods, such as finite element analysis and statistical energy analysis, to simulate the acoustic performance of SPC flooring in actual applications and output acoustic performance evaluation results.

[0069] The technical solution of the present invention effectively solves the problem of the lack of a dynamic adjustment mechanism for evaluation weights in traditional acoustic evaluations, thereby significantly improving the accuracy and practicality of acoustic performance evaluations. By utilizing historical performance data, this method not only enhances the continuity of responses to material and structural changes, but also enables real-time adjustment of the evaluation model to ensure that the evaluation results reflect the latest situation in actual applications. This efficient evaluation process not only shortens the product development cycle and reduces costs, but also accurately predicts acoustic effects.

[0070] Further, if Figure 2 and Figure 5 As shown in Figure 2, acoustic response weights are assigned to several structural acoustic assessment factors, including:

[0071] S210: Collect historical attribute parameters of the SPC floor, assign initial evaluation weights based on the historical attribute parameters of the SPC floor and several structural acoustic evaluation factors, and calculate the initial total score based on the initial evaluation weights;

[0072] S220: Based on the initial evaluation weight, mark any structural acoustic evaluation factor as a variable factor and the remaining structural acoustic evaluation factors as constant factors, increase or decrease the initial evaluation weight of the variable factor, calculate the total score, and compare the total score with the initial total score to obtain the change in the total score;

[0073] S230: Cancel the mark of the variable factor and select any constant factor to be marked as the variable factor. The cancelled variable factor is used as the constant factor again. The initial evaluation weight of the variable factor is increased or decreased respectively. The total score is calculated and compared with the initial total score to obtain the change in the total score.

[0074] S240: Repeat marking the variable factors until all structural acoustics assessment factors serve as variable factors, and obtain the total score change of the corresponding variable factors;

[0075] S250: Based on the change in the total score, as the influence of several structural acoustic assessment factors on the structural acoustic performance assessment, acoustic response weights are allocated according to the influence.

[0076] As a preferred embodiment of the above, the historical attribute parameters of the SPC floor are first collected, which include the actual performance data of the floor during use, such as the acoustic performance results of past tests (sound wave transmission loss and impact sound level, etc.), and the physical and chemical attribute records of the floor, such as material type, layer structure, density and thickness. These historical attribute parameters provide key baseline information for the subsequent evaluation steps. According to the correlation between the collected historical attribute parameters and the acoustic evaluation factors, an initial evaluation weight is assigned to each factor. These initial weights are based on the historical performance data and the expected performance requirements to calculate the initial total score, which is used as the basis of the evaluation model. Then, one structural acoustic evaluation factor is selected as a variable factor, that is, as a variable, and the remaining factor is used as a constant factor, that is, a factor that keeps the weight unchanged. Then, by adjusting The weight of the variable factor is increased or decreased to observe the change in the total score. After obtaining the change in the total score of a structural acoustic assessment factor, the previously marked variable factor is cancelled and a new constant factor is selected as the new variable factor to perform the same process. Each factor will be tested as a variable factor in turn to ensure a comprehensive evaluation of the impact of all factors. Based on the change in the total score shown by each factor in the test, the acoustic response weight is redistributed. Factors with large changes will receive higher weights, which reflects their importance in improving or affecting acoustic performance. For example, increasing or decreasing the sound wave transmission loss can significantly affect the total score, while an increase or decrease in the impact sound level can only have a smaller impact on the total score. This means that the actual weight of the sound wave transmission loss should be greater than the actual weight of the impact sound level and have a higher influence.

[0077] Furthermore, if Figure 3 As shown, the initial total score is calculated based on the initial evaluation weights, including:

[0078] S1: Collect historical acoustic test parameters, perform performance analysis on the historical acoustic test parameters according to the control variable method, obtain performance analysis results, assign scores to several structural acoustic evaluation factors based on the performance analysis results, and obtain individual scores corresponding to several structural acoustic evaluation factors;

[0079] S2: Integrate the individual scores corresponding to several structural acoustic assessment factors with the initial assessment weights to obtain the individual contributions of the corresponding structural acoustic assessment factors. Combine the individual contributions corresponding to several structural acoustic assessment factors to obtain the initial total score.

[0080] The total score change is obtained by calculating the total score according to steps S1 and S2, and then calculating the difference between the total score and the initial total score to obtain the total score change.

[0081] In this embodiment, acoustic test data for SPC flooring is first collected from past test records. Then, controlled variables are used to determine variables that remain constant during the analysis period, including test temperature, humidity, and test equipment. Several test data sets are obtained, and statistical analysis is performed on each set of test data, such as calculating statistical indicators such as mean and variance to obtain performance analysis results. A performance benchmark is then set for each acoustic test to determine thresholds for excellent performance and substandard performance. Each test result is scored based on the performance analysis results. For example, a scoring system from 1 to 10 can be set, where 10 indicates performance significantly above the benchmark and 1 indicates performance significantly below the benchmark. The score can be quantified based on the difference between the test result and the benchmark. For example, if the STL value exceeds the industry standard by more than 10%, a high score (e.g., 9 or 10) may be assigned; if it is less than 10%, a low score (e.g., 1 or 2) may be assigned. The individual contribution of each structural acoustic assessment factor is calculated based on the score and the initial assessment weight. The individual contribution of each factor is summed to calculate the initial total score. The change in the total score is also obtained using the same method.

[0082] Furthermore, if Figure 4 As shown in Figure 2, several structural acoustic assessment factors are assigned combined secondary weights based on acoustic construction properties, including:

[0083] Several secondary acoustic evaluation factors are established based on the properties of the noise-proof materials and structures. These factors include the ratio of natural stone powder to polyvinyl chloride, and the proportions of the core layer, wear-resistant layer, and base layer.

[0084] The acoustic influence standard is set using a scaling method. The ratio of natural stone powder to polyvinyl chloride and the proportion of the core layer, wear-resistant layer, and bottom layer are evaluated according to the acoustic influence standard to obtain an evaluation value. The evaluation is performed by separately evaluating the material properties of natural stone powder and polyvinyl chloride and the structural properties of the proportion of the core layer, wear-resistant layer, and bottom layer.

[0085] According to the evaluation values, a secondary factor evaluation matrix is constructed respectively. The rows and columns of the secondary factor evaluation matrix are secondary acoustic evaluation factors, and the element values in the matrix are evaluation values.

[0086] A mathematical method is used to calculate the maximum eigenvalue of the secondary factor evaluation matrix, and a corresponding eigenvector is obtained according to the maximum eigenvalue. The eigenvector is normalized to obtain a normalized eigenvector, and several element values in the normalized eigenvector represent the secondary evaluation weights of the secondary acoustic evaluation factors;

[0087] The secondary evaluation weight of the noise prevention material property is combined with the secondary evaluation weight of the noise prevention structure property to obtain a combined secondary weight.

[0088] Specifically, first, the secondary acoustic evaluation factors are clearly defined according to the material and structural characteristics of the SPC floor. For example, the ratio of natural stone powder to polyvinyl chloride not only affects the hardness and durability of the floor, but also affects the transmission of sound. Similarly, the proportion of the core layer, wear-resistant layer and bottom layer is related to the absorption and reflection characteristics of sound. Then, a scaling method is used to define a scoring system with a scale of 1 to 9, where 1 represents the lowest influence and 9 represents the highest influence. Specific standards are set for each level. For example, if a design change increases the STL value by more than 5dB, it may be rated 9; if the change is not obvious, such as less than 1dB, it is rated 1. Then, the secondary factors of material and structural properties are scored separately. For example, the impact of changes in the ratio of natural stone powder to polyvinyl chloride on acoustic performance is examined, and corresponding scores are given according to preset standards to create an evaluation matrix in which rows and columns are Represent different secondary factors and fill in the matrix according to the scoring of the scaling method. Each element represents the score of a specific secondary factor under given conditions. Mathematical methods such as eigenvalue and eigenvector theory are used to calculate the eigenvalues of the secondary factor evaluation matrix. Special attention is paid to the largest eigenvalue because it represents the most significant direction of variation in the matrix, that is, the most important combination of evaluation factors. The eigenvector corresponding to the largest eigenvalue is extracted. Each component in this vector corresponds to the relative importance of a secondary factor. This eigenvector is then normalized. The purpose of normalization is to make the sum of the weights of all factors equal to 1, so that each weight represents the contribution ratio of the factor to the overall acoustic performance. Each element value in the normalized eigenvector now represents the standardized weight of each secondary acoustic evaluation factor. The weights of the secondary factors of structural and material properties are combined to obtain the combined secondary weight.

[0089] Furthermore, the acoustic evaluation model is constructed, including:

[0090] Construct a weight verification database, which includes historical property parameters and historical acoustic test parameters of SPC flooring;

[0091] The mapping relationship between the historical attribute parameters of the SPC floor and the historical acoustic test parameters is extracted from the weight verification database, and a number of historical acoustic response weights and historical combination secondary weights are obtained based on the mapping relationship;

[0092] The acoustic response weight and the combined secondary weight are analyzed for deviation according to the historical acoustic response weight and the historical combined secondary weight. The acoustic response weight and the combined secondary weight are optimized according to the deviation analysis results to obtain the optimization results. The acoustic evaluation model is constructed according to the optimization results.

[0093] As a preferred embodiment of the above, by using the collected historical attribute parameters of the SPC floor, such as the type, thickness, density, etc., and the acoustic test evaluation parameters including acoustic absorption rate, sound insulation performance index, etc., a weight verification database is constructed. The database can be queried and data processed using the Python Pandas library. This database not only stores historical data, but also supports in-depth analysis of these data. A mapping relationship is established using a linear regression or decision tree algorithm. This mapping relationship can be obtained by establishing a mapping model, training it through a machine learning algorithm, and cross-validating the trained model. Through this method, the weight of the acoustic evaluation model can be dynamically adjusted according to historical data and real-time test results. According to the extracted mapping relationship, the historical acoustic response weight and the historical combination secondary weight are obtained. The deviation analysis is performed by combining the historical weight distribution with the actual weight distribution. The weight can be optimized using a multi-objective optimization strategy or a genetic algorithm. The optimized weight parameters are used in combination with finite element analysis software such as ANSYS or ABAQUS to establish an acoustic evaluation model of the SPC floor.

[0094] Furthermore, a weight verification database is constructed, including:

[0095] Extract historical material attribute information and historical structural attribute information based on the historical attribute parameters of the SPC floor to obtain an acoustic evaluation feature set;

[0096] Use machine learning algorithms to perform deep learning on the acoustic evaluation feature set to obtain deep learning results;

[0097] Determine the standard material ratio and standard structure proportion based on the deep learning results, and allocate the secondary weight of the historical combination based on the standard material ratio and standard structure proportion;

[0098] Perform secondary learning on historical combination secondary weights and historical acoustic test parameters to obtain historical acoustic response weights, and establish a mapping relationship based on the historical combination secondary weights and historical acoustic response weights;

[0099] The mapping relationship and historical acoustic test parameters are combined and indexed to build a weight verification database.

[0100] In this embodiment, historical attribute parameters of SPC flooring, such as material type (e.g., PVC content, additive type), layer structure (e.g., core layer thickness, wear-resistant layer material), and acoustic performance indicators (e.g., sound absorption rate and sound insulation), are first extracted. This data is then organized into a set of acoustic evaluation features. Next, these features are analyzed using deep learning algorithms, such as neural networks. Cross-validation techniques can be used to fine-tune parameters to ensure that the model extracted from the historical data can accurately predict acoustic performance. The model is trained using machine learning frameworks such as TensorFlow or PyTorch. The deep learning results are used to determine the optimal material ratio and structural proportion. These results are then used to guide material usage and process adjustments in production. Furthermore, a second round of learning and genetic algorithm optimization is performed on the optimized historical combination secondary weights and actual acoustic test parameters to fine-tune the weights to reduce the deviation between the predicted and actual data. Finally, these optimized weights and mapping relationships are integrated into a weight verification database. This database provides direct data support for future design and production, ensuring that through systematic data analysis and advanced machine learning techniques, the evaluation and optimization of acoustic performance are more scientific and accurate, thereby effectively improving product quality and meeting market demand.

[0101] Furthermore, a deviation analysis is performed on the acoustic response weight and the combined secondary weight based on the historical acoustic response weight and the historical combined secondary weight, and the combined secondary weight of the acoustic response weight is optimized based on the deviation analysis result, including:

[0102] The historical acoustic response weight and the historical combined secondary weight are respectively used as standard values, and the acoustic response weight and the combined secondary weight are respectively used as actual values, and the deviation analysis is performed respectively using the loss function to obtain the deviation analysis results;

[0103] Identify the main deviation factors based on the deviation results, re-evaluate the weights of the main deviation factors, and use the loss function to perform deviation analysis again based on the re-evaluation results to obtain the secondary deviation analysis results;

[0104] Iterative optimization is performed based on the results of the quadratic deviation analysis to obtain the optimization results.

[0105] Specifically, first, extract the historical acoustic response weights and historical combined secondary weights from the weight verification database, set these historical weights as the benchmark standard values for the evaluation model, and at the same time, collect the acoustic response weights and combined secondary weights of the current production batch, use them as actual values for the current deviation analysis, and use mathematical loss functions, such as mean square error (MSE) or mean absolute error (MAE), to quantify the deviation between the historical weights (standard values) and the actual measured weights (actual values). According to the results of the loss function, identify the acoustic response weights and combined secondary weight factors that cause the maximum deviation, conduct in-depth analysis of these major deviation factors, and explore the possible reasons behind them, such as changes in material properties. , deviations in the production process, etc., based on the identified main deviation factors, the weights of these factors are accurately re-evaluated, and the reasonable values of these weights are recalculated taking into account the actual production conditions and historical performance data. After re-adjusting the weights, the deviation analysis is performed again using the same loss function to check the effect of the adjusted weights to ensure that the deviation can be significantly reduced through adjustment. According to the results of the secondary deviation analysis, if there is still an unacceptable deviation, the steps of deviation identification and weight adjustment are repeated. This iterative process continues until the preset deviation threshold is reached or the deviation is no longer significantly reduced. After multiple rounds of iterations, a set of optimized acoustic response weights and combined secondary weights are finally obtained, and the weight optimization results are obtained.

[0106] Furthermore, the deviation analysis performed using loss functions includes:

[0107] ;

[0108] in, Indicates the actual value, Indicates the standard value, is the number of acoustic response weights or combined sub-weights, Indicates the deviation analysis results.

[0109] As a preference of the above embodiment, according to the actual value and standard value defined in the deviation analysis, the square error loss function is selected to perform deviation analysis. This function calculates the square of the difference between the actual value and the standard value. The square error loss function is selected because it imposes a higher penalty on large deviations, which helps to clarify significant deviations in the weights. All weights are iterated, and the difference between each pair of actual values and standard values is calculated, and the squares of these differences are summed to obtain the total deviation. The calculation of the total deviation provides a quantitative deviation measure for the data. The total deviation calculated by the loss function is analyzed to determine which weights have deviations that exceed the acceptable range. These weights are marked as major deviation factors and require further adjustment and optimization.

[0110] Embodiment 2;

[0111] Based on the same inventive concept as the method for evaluating the acoustic performance of an SPC floor noise-proof structure in the aforementioned embodiment, the present invention further provides an SPC floor noise-proof structure acoustic performance evaluation system, the system comprising:

[0112] The evaluation factor setting module collects the acoustic structural properties of the SPC floor and establishes several structural acoustic evaluation factors based on the acoustic performance evaluation requirements of the SPC floor. The acoustic structural properties include the noise-proof material properties and the noise-proof structure properties.

[0113] A multi-weight assignment module assigns acoustic response weights to several structural acoustic assessment factors and assigns combined secondary weights to several structural acoustic assessment factors based on acoustic construction properties;

[0114] The model construction and output module constructs an acoustic evaluation model and outputs the structural acoustic performance evaluation results based on the acoustic evaluation model combined with the acoustic response weight and the combined secondary weight.

[0115] The above-mentioned adjustment system in the present invention can effectively implement the acoustic performance evaluation method of the SPC floor noise insulation structure, and the technical effects that can be achieved are as described in the above embodiments and will not be repeated here.

[0116] Specifically, the model building and output modules include:

[0117] A weight verification database construction unit is used to construct a weight verification database, which includes historical attribute parameters and historical acoustic test parameters of the SPC floor;

[0118] The historical weight data extraction unit extracts the mapping relationship between the historical attribute parameters of the SPC floor and the historical acoustic test parameters according to the weight verification database, and obtains a number of historical acoustic response weights and historical combination secondary weights according to the mapping relationship;

[0119] The weight deviation analysis and optimization unit performs deviation analysis on the acoustic response weight and the combined secondary weight according to the historical acoustic response weight and the historical combined secondary weight, optimizes the acoustic response weight and the combined secondary weight according to the deviation analysis results, obtains the optimization results, and constructs the acoustic evaluation model according to the optimization results.

[0120] Similarly, the above-mentioned optimization schemes for the system can also respectively achieve the corresponding optimization effects of the method in Example 1, which will not be repeated here.

[0121] Although the present application has been described with reference to specific features and embodiments thereof, it is apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and drawings are merely illustrative of the present application as defined herein and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, the present application is intended to include such modifications and variations as fall within the scope of the present application and its equivalents.

Claims

1. A method for evaluating the acoustic performance of SPC floor noise-proof structure, characterized in that: The method comprises: Acoustic structural properties of the SPC floor are collected, and several structural acoustic evaluation factors are established according to the acoustic performance evaluation requirements of the SPC floor, wherein the acoustic structural properties include noise-proof material properties and noise-proof structural properties; Allocating acoustic response weights to the plurality of structural acoustic assessment factors, and allocating combined secondary weights to the plurality of structural acoustic assessment factors according to the acoustic construction properties, including: Establishing several secondary acoustic evaluation factors according to the properties of the noise-proof material and the noise-proof structure, wherein the secondary acoustic evaluation factors include the ratio of natural stone powder to polyvinyl chloride, and the proportions of the core layer, the wear-resistant layer, and the bottom layer; An acoustic influence standard is set using a scaling method, and the ratio of the natural stone powder to the polyvinyl chloride and the proportion of the core layer, the wear-resistant layer, and the bottom layer are evaluated according to the acoustic influence standard to obtain an evaluation value, wherein the evaluation is to separately evaluate the material properties of the natural stone powder and the polyvinyl chloride and the structural properties of the proportion of the core layer, the wear-resistant layer, and the bottom layer; constructing secondary factor evaluation matrices according to the evaluation values, wherein the rows and columns of the secondary factor evaluation matrix are the secondary acoustic evaluation factors, and the element values in the matrix are the evaluation values; Calculating the maximum eigenvalue of the secondary factor evaluation matrix using a mathematical method, obtaining a corresponding eigenvector according to the maximum eigenvalue, normalizing the eigenvector to obtain a normalized eigenvector, wherein several element values in the normalized eigenvector represent secondary evaluation weights of the secondary acoustic evaluation factors; combining the secondary evaluation weight of the noise-proofing material property with the secondary evaluation weight of the noise-proofing structure property to obtain a combined secondary weight; An acoustic evaluation model is constructed, and a structural acoustic performance evaluation result is output according to the acoustic evaluation model in combination with the acoustic response weight and the combined secondary weight.

2. The method for evaluating the acoustic performance of the SPC floor noise-proof structure according to claim 1, characterized in that: Acoustic response weights are assigned to several structural acoustic assessment factors, including: Collecting historical attribute parameters of the SPC floor, assigning initial evaluation weights according to the historical attribute parameters of the SPC floor and a number of the structural acoustic evaluation factors, and calculating an initial total score according to the initial evaluation weights; Based on the initial evaluation weight, mark any one of the structural acoustics evaluation factors as a variable factor and the remaining structural acoustics evaluation factors as constant factors, increase or decrease the initial evaluation weight of the variable factor, calculate a total score, and compare the total score with the initial total score to obtain a change in the total score; Cancel the mark of the variable factor, select any of the constant factors as the variable factor, set the canceled variable factor as the constant factor again, increase or decrease the initial evaluation weight of the variable factor respectively, calculate the total score, and compare it with the initial total score to obtain the change in the total score; Repeating the step of marking the variable factors until all the structural acoustics assessment factors serve as the variable factors, and obtaining the total score changes corresponding to the variable factors; According to the total score change, as the influence of the plurality of structural acoustics assessment factors on the structural acoustics performance assessment, acoustic response weights are allocated according to the influence.

3. The method for evaluating the acoustic performance of the SPC floor noise-proof structure according to claim 2, characterized in that: Calculating the initial total score according to the initial evaluation weights includes: S1: Collect historical acoustic test parameters, perform performance analysis on the historical acoustic test parameters according to a control variable method, obtain performance analysis results, assign scores to several structural acoustic evaluation factors based on the performance analysis results, and obtain individual scores corresponding to the several structural acoustic evaluation factors; S2: integrating the individual scores corresponding to the plurality of structural acoustics assessment factors with the initial assessment weights to obtain the individual contributions corresponding to the structural acoustics assessment factors, and combining the individual contributions corresponding to the plurality of structural acoustics assessment factors to obtain an initial total score; The total score change is obtained by calculating the total score according to steps S1 and S2, and calculating the difference between the total score and the initial total score to obtain the total score change.

4. The method for evaluating the acoustic performance of the SPC floor noise-proof structure according to claim 1, characterized in that: Build an acoustic assessment model, including: Constructing a weight verification database, wherein the weight verification database includes historical attribute parameters and historical acoustic test parameters of the SPC floor; Extracting a mapping relationship between the historical attribute parameters of the SPC floor and the historical acoustic test parameters according to the weight verification database, and obtaining a plurality of historical acoustic response weights and historical combined secondary weights according to the mapping relationship; Performing a deviation analysis on the acoustic response weight and the combined secondary weight according to the historical acoustic response weight and the historical combined secondary weight, optimizing the acoustic response weight and the combined secondary weight according to the deviation analysis result, obtaining an optimization result, and constructing an acoustic evaluation model according to the optimization result.

5. The method for evaluating the acoustic performance of the SPC floor noise-proof structure according to claim 4, characterized in that: Build a weight verification database, including: Extracting historical material attribute information and historical structural attribute information based on the historical attribute parameters of the SPC floor to obtain an acoustic evaluation feature set; Performing deep learning on the acoustic evaluation feature set using a machine learning algorithm to obtain a deep learning result; Determine a standard material ratio and a standard structure proportion based on the deep learning results, and assign a secondary weight of the historical combination based on the standard material ratio and the standard structure proportion; Performing secondary learning on the historical combined secondary weights and the historical acoustic test parameters to obtain historical acoustic response weights, and establishing a mapping relationship between the historical combined secondary weights and the historical acoustic response weights; The mapping relationship and historical acoustic test parameters are combined and indexed to construct a weight verification database.

6. The method for evaluating the acoustic performance of the SPC floor noise-proof structure according to claim 4, characterized in that: Performing a deviation analysis on the acoustic response weight and the combined secondary weight according to the historical acoustic response weight and the historical combined secondary weight, and optimizing the acoustic response weight and the combined secondary weight according to the deviation analysis result, including: The historical acoustic response weight and the historical combined secondary weight are respectively used as standard values, and the acoustic response weight and the combined secondary weight are respectively used as actual values, and deviation analysis is performed respectively using a loss function to obtain deviation analysis results; Identifying main deviation factors according to the deviation analysis results, re-evaluating weights of the main deviation factors, and performing deviation analysis again using the loss function according to the re-evaluation results to obtain secondary deviation analysis results; Iterative optimization is performed based on the secondary deviation analysis result to obtain an optimization result.

7. The method for evaluating the acoustic performance of the SPC floor noise-proof structure according to claim 6, characterized in that: Deviation analysis using loss functions includes: ； in, represents the actual value, Indicates the standard value, is the number of acoustic response weights or combined sub-weights, Indicates the deviation analysis results. 8.SPC floor noise-proof structural acoustic performance evaluation system, characterized by: The method for evaluating the acoustic performance of the SPC floor noise-proof structure according to claim 1 is adopted, and the system includes: An evaluation factor setting module collects the acoustic structural properties of the SPC floor and establishes several structural acoustic evaluation factors based on the acoustic performance evaluation requirements of the SPC floor. The acoustic structural properties include noise-proof material properties and noise-proof structural properties. a multi-weight assignment module for assigning acoustic response weights to each of the plurality of structural acoustic assessment factors, and assigning combined secondary weights to each of the plurality of structural acoustic assessment factors according to the acoustic construction properties; The model construction and output module constructs an acoustic evaluation model and outputs a structural acoustic performance evaluation result according to the acoustic evaluation model in combination with the acoustic response weight and the combined secondary weight.

9. The SPC floor noise-proof structural acoustic performance evaluation system according to claim 8, characterized in that: The model building and output module includes: A weight verification database construction unit is configured to construct a weight verification database, wherein the weight verification database includes historical attribute parameters and historical acoustic test parameters of the SPC floor; a historical weight data extraction unit, which extracts a mapping relationship between the historical attribute parameters of the SPC floor and the historical acoustic test parameters according to the weight verification database, and obtains a plurality of historical acoustic response weights and historical combined secondary weights according to the mapping relationship; The weight deviation analysis and optimization unit performs deviation analysis on the acoustic response weight and the combined secondary weight according to the historical acoustic response weight and the historical combined secondary weight, optimizes the acoustic response weight and the combined secondary weight according to the deviation analysis result, obtains the optimization result, and constructs the acoustic evaluation model according to the optimization result.

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